



"""simple docstring"""







import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
			import onnxruntime as ort






class       UpperCamelCase__			(   _lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


			pass






@nightly
@require_onnxruntime
@require_torch_gpu
class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			@property
			def   snake_case__	(      self		)      ->	Tuple:
				return (
				    "CUDAExecutionProvider",
				    {
				        "gpu_mem_limit": "15000000000",  # 15GB
				        "arena_extend_strategy": "kSameAsRequested",
				    },
				)


			@property
			def   snake_case__	(      self		)      ->	Any:
				A__											=					ort.SessionOptions()
				A__											=					False
				return options


			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo.png"		)
				A__											=					load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"		)
				A__											=					OnnxStableDiffusionInpaintPipeline.from_pretrained(
				    "runwayml/stable-diffusion-inpainting"						,	revision="onnx"						,	safety_checker=SCREAMING_SNAKE_CASE__						,	feature_extractor=SCREAMING_SNAKE_CASE__						,	provider=self.gpu_provider						,	sess_options=self.gpu_options						,	)
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__		)

				A__											=					"A red cat sitting on a park bench"

				A__											=					np.random.RandomState(0		)
				A__											=					pipe(
				    prompt=SCREAMING_SNAKE_CASE__						,	image=SCREAMING_SNAKE_CASE__						,	mask_image=SCREAMING_SNAKE_CASE__						,	guidance_scale=7.5						,	num_inference_steps=10						,	generator=SCREAMING_SNAKE_CASE__						,	output_type="np"						,	)
				A__											=					output.images
				A__											=					images[0, 255:258, 255:258, -1]

				assert images.shape == (1, 512, 512, 3)
				A__											=					np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3







			def   snake_case__	(      self		)      ->	str:
				A__											=					load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo.png"		)
				A__											=					load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"		)
				A__											=					LMSDiscreteScheduler.from_pretrained(
				    "runwayml/stable-diffusion-inpainting"						,	subfolder="scheduler"						,	revision="onnx"		)
				A__											=					OnnxStableDiffusionInpaintPipeline.from_pretrained(
				    "runwayml/stable-diffusion-inpainting"						,	revision="onnx"						,	scheduler=SCREAMING_SNAKE_CASE__						,	safety_checker=SCREAMING_SNAKE_CASE__						,	feature_extractor=SCREAMING_SNAKE_CASE__						,	provider=self.gpu_provider						,	sess_options=self.gpu_options						,	)
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__		)

				A__											=					"A red cat sitting on a park bench"

				A__											=					np.random.RandomState(0		)
				A__											=					pipe(
				    prompt=SCREAMING_SNAKE_CASE__						,	image=SCREAMING_SNAKE_CASE__						,	mask_image=SCREAMING_SNAKE_CASE__						,	guidance_scale=7.5						,	num_inference_steps=20						,	generator=SCREAMING_SNAKE_CASE__						,	output_type="np"						,	)
				A__											=					output.images
				A__											=					images[0, 255:258, 255:258, -1]

				assert images.shape == (1, 512, 512, 3)
				A__											=					np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case				:      Any   		=			logging.get_logger(__name__)

__snake_case				:      Union[str, Any]   		=			{
    """microsoft/cvt-13""": """https://huggingface.co/microsoft/cvt-13/resolve/main/config.json""",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}



class        A__(a_							):
						"""simple docstring"""






						_A    :				Union[str, Any]       					=			'''cvt'''





						def __init__(    self  ,      _lowercase=3  ,      _lowercase=[7, 3, 3]  ,      _lowercase=[4, 2, 2]  ,      _lowercase=[2, 1, 1]  ,      _lowercase=[64, 192, 384]  ,      _lowercase=[1, 3, 6]  ,      _lowercase=[1, 2, 10]  ,      _lowercase=[4.0, 4.0, 4.0]  ,      _lowercase=[0.0, 0.0, 0.0]  ,      _lowercase=[0.0, 0.0, 0.0]  ,      _lowercase=[0.0, 0.0, 0.1]  ,      _lowercase=[True, True, True]  ,      _lowercase=[False, False, True]  ,      _lowercase=["dw_bn", "dw_bn", "dw_bn"]  ,      _lowercase=[3, 3, 3]  ,      _lowercase=[1, 1, 1]  ,      _lowercase=[2, 2, 2]  ,      _lowercase=[1, 1, 1]  ,      _lowercase=[1, 1, 1]  ,      _lowercase=0.0_2  ,      _lowercase=1e-12  ,      **_lowercase  ,      )     -> Tuple:
									super().__init__(**_lowercase    )
									a_						:						str 							= num_channels
									a_						:						Optional[int] 							= patch_sizes
									a_						:						Dict 							= patch_stride
									a_						:						int 							= patch_padding
									a_						:						int 							= embed_dim
									a_						:						Tuple 							= num_heads
									a_						:						Optional[Any] 							= depth
									a_						:						str 							= mlp_ratio
									a_						:						Optional[int] 							= attention_drop_rate
									a_						:						int 							= drop_rate
									a_						:						List[Any] 							= drop_path_rate
									a_						:						List[Any] 							= qkv_bias
									a_						:						Dict 							= cls_token
									a_						:						Union[str, Any] 							= qkv_projection_method
									a_						:						List[Any] 							= kernel_qkv
									a_						:						Optional[Any] 							= padding_kv
									a_						:						Any 							= stride_kv
									a_						:						Dict 							= padding_q
									a_						:						Optional[int] 							= stride_q
									a_						:						Tuple 							= initializer_range
									a_						:						int 							= layer_norm_eps


